Transformer Based Multi-Grained Attention Network for Aspect-Based Sentiment Analysis
نویسندگان
چکیده
منابع مشابه
Multi-Granular Aspect Aggregation in Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis estimates the sentiment expressed for each particular aspect (e.g., battery, screen) of an entity (e.g., smartphone). Different words or phrases, however, may be used to refer to the same aspect, and similar aspects may need to be aggregated at coarser or finer granularities to fit the available space or satisfy user preferences. We introduce the problem of aspec...
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Sentiment analysis aims to determine the evaluation of an author with respect to a particular topic and detecting the overall contextual polarity of a document. Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint. The majority of current approaches, however, attempt to detect the overall polarity of a sentence, paragraph, or text span, irr...
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We propose a novel framework based on neural networks to identify the sentiment of opinion targets in a comment/review. Our framework adopts multiple-attention mechanism to capture sentiment features separated by a long distance, so that it is more robust against irrelevant information. The results of multiple attentions are non-linearly combined with a recurrent neural network, which strengthe...
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Deep learning techniques have achieved success in aspect-based sentiment analysis in recent years. However, there are two important issues that still remain to be further studied, i.e., 1) how to efficiently represent the target especially when the target contains multiple words; 2) how to utilize the interaction between target and left/right contexts to capture the most important words in them...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3039470